Michaels' Google-Powered AI Assistant Doubles Conversion Rate vs Traditional Search

Ask Mike: 2x Conversion, 27% Interaction Rate

Arts and crafts retailer Michaels has revealed striking performance data for its AI shopping assistant "Ask Mike," built on Google Cloud's Gemini Enterprise platform. The assistant converts visitors at more than double the rate of traditional keyword search, with 27% of all interactions resulting in a product click or add-to-cart action. These numbers provide some of the strongest evidence yet that AI-powered conversational commerce outperforms conventional ecommerce search.

Ask Mike officially launched on July 21, 2026, following a quiet beta period that began in May. During that beta phase, the assistant handled approximately 75,000 conversations—generating enough data for Michaels to validate the technology's impact before committing to a full public launch.

Why Conversational AI Outperforms Keyword Search

The performance gap between Ask Mike and traditional search reveals a fundamental limitation of keyword-based product discovery. When a shopper types "yarn" into a conventional search bar, they receive a list of results filtered by basic attributes. They must then refine their search, browse categories, compare products, and make a decision—often requiring multiple search iterations.

With Ask Mike, the same shopper can say "I need soft, washable yarn for a baby blanket in pastel colors" and receive a curated selection of matching products in a single interaction. The AI understands the project context, the material requirements, and the aesthetic preferences, filtering thousands of products down to the most relevant options.

This natural language interaction model is particularly powerful in the crafts category, where purchases are often project-based. A customer planning a wedding decoration project might need products from multiple categories—fabric, ribbon, adhesive, decorative elements—that would require separate searches in a traditional interface. Ask Mike can recommend a complete set of supplies based on a single project description.

The Bundling Effect: More Items Per Session

One of the most valuable findings from Michaels' data is that AI-assisted shoppers tend to bundle multiple items in a single session. When a customer describes a project to Ask Mike, the assistant can suggest complementary products the shopper might not have considered. A customer looking for painting supplies might receive recommendations for brushes, canvas, paint, easels, and cleanup supplies—all in one interaction.

This bundling effect directly increases average order value. Traditional search tends to produce single-item purchases: the customer searches for what they know they need and buys it. Conversational AI, by contrast, acts like a knowledgeable store associate who suggests everything required for a project, including items the customer didn't know they needed.

Built on Google Cloud in Six Weeks

Heather Bennett, Michaels' technology leader, emphasized that Ask Mike was built on Google Cloud infrastructure in approximately six weeks—a timeline that demonstrates how rapidly enterprise AI assistants can now be deployed. The speed was possible because Google Cloud provides pre-built commerce AI components that retailers can customize rather than building from scratch.

Kapil Dabi of Google Cloud noted that Michaels is part of a growing cohort of retailers using the same Google Cloud platform, including Ulta Beauty, Macy's, and Home Depot. This shared infrastructure approach means the technology improves as more retailers adopt it, with learnings from one deployment informing improvements across the platform.

What This Means for Shopify Merchants

Michaels' results have direct implications for Shopify merchants, even those operating at a much smaller scale:

1. Conversational search outperforms keyword search. If your Shopify store relies on basic keyword search, you may be leaving conversions on the table. AI-powered search tools for Shopify—such as Shopify's native AI search or third-party apps—can replicate the Ask Mike experience at scale.
2. Project-based product recommendations increase AOV. Consider how your products fit into larger customer projects. Structuring product data with use-case tags, project associations, and complementary product links enables AI systems to make intelligent bundle recommendations.
3. Natural language product data matters. Product descriptions that describe use cases, materials, and project applications—rather than just specifications—perform better with AI assistants.
4. Speed of deployment is achievable. Enterprise-grade AI assistants can be deployed in weeks, not months. Shopify merchants can start with simpler AI search tools and progressively upgrade as the technology matures.